Computational simulations reveal the binding dynamics between human ACE2 and the receptor binding domain of SARS-CoV-2 spike protein

Abstract

Background: A novel coronavirus (the SARS-CoV-2) has been identified in January 2020 as the causal pathogen for COVID-19 , a pandemic started near the end of 2019. The Angiotensin converting enzyme 2 protein (ACE2) utilized by the SARS-CoV as a receptor was found to facilitate the infection of SARS-CoV-2, initiated by the binding of the spike protein to human ACE2.

Methods: Using homology modeling and molecular dynamics (MD) simulation methods, we report here the detailed structure and dynamics of the ACE2 in complex with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein.

Results: The predicted model is highly consistent with the experimentally determined structures, validating the homology modeling results. Besides the binding interface reported in the crystal structures, novel binding poses are revealed from all-atom MD simulations. The simulation data are used to identify critical residues at the complex interface and provide more details about the interactions between the SARS-CoV-2 RBD and human ACE2.

Conclusion: Simulations reveal that RBD binds to both open and closed state of ACE2. Two human ACE2 mutants and rat ACE2 are modeled to study the mutation effects on RBD binding to ACE2. The simulations show that the N-terminal helix and the K353 are very important for the tight binding of the complex, the mutants are found to alter the binding modes of the CoV2-RBD to ACE2.

Graphical abstract

Keywords

SARS-CoV-2 / COVID-19 / ACE2 / mutation / molecular dynamics simulations

References

[1] Zhu, N., Zhang, D., Wang, W.,Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W.,Lu, R., (2020) A Novel Coronavirus from Patients with Pneumonia in China, 2019. N. Engl. J. Med., NEJMoa2001017. https://doi.org/10.1056/nejmoa2001017.

[2] Wu, F., Zhao, S., Yu, B., Chen, Y.-M., Wang, W., Song, Z.-G., Hu, Y., Tao, Z.-W., Tian, J.-H., Pei, Y.-Y., (2020) A new coronavirus associated with human respiratory disease in China. Nature, 579, 265–269.

[3] Othman, H., Bouslama, Z., Brandenburg, J.T., da Rocha, J., Hamdi, Y., Ghedira, K., Srairi-Abid, N. and Hazelhurst, S. (2020) Interaction of the spike protein RBD from SARS-CoV-2 with ACE2: Similarity with SARS-CoV, hot-spot analysis and effect of the receptor polymorphism. Biochem. Biophys. Res. Commun., 527, 702–708.

[4] Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., Wang, W., Song, H., Huang, B., Zhu, N., (2020) Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet, 395, 565–574.

[5] Li, W., Moore, M. J., Vasilieva, N., Sui, J., Wong, S. K., Berne, M. A., Somasundaran, M., Sullivan, J. L., Luzuriaga, K., Greenough, T. C., (2003) Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature, 426, 450–454.

[6] Wong, S. K., Li, W., Moore, M. J., Choe, H. and Farzan, M. (2004) A 193-amino acid fragment of the SARS coronavirus S protein efficiently binds angiotensin-converting enzyme 2. J. Biol. Chem., 279, 3197–3201.

[7] Li, F., Li, W., Farzan, M. and Harrison, S. C. (2005) Structure of SARS coronavirus spike receptor-binding domain complexed with receptor. Science, 309, 1864–1868.

[8] Kuba, K., Imai, Y., Ohto-Nakanishi, T. and Penninger, J. M. (2010) Trilogy of ACE2: a peptidase in the renin-angiotensin system, a SARS receptor, and a partner for amino acid transporters. Pharmacol. Ther., 128, 119–128.

[9] Bao, L.,Deng, W.,Huang, B.,Gao, H.,Ren, L., Wei, Q., Yu, P., Xu, Y., Liu, J., Qi, F., Qu, Y., (2020)The pathogenicity of 2019 novel coronavirus in hACE2 transgenic mice, BioRxiv, 2020.02.07.939389. https://doi.org/10.1101/2020.02.07.939389.

[10] Lan, J., Ge, J., Yu, J., Shan, S., Zhou, H., Fan, S., Zhang, Q., Shi, X., Wang, Q., Zhang, L., (2020) Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature, 581, 215–220.

[11] Wang, Q., Zhang, Y., Wu, L., Niu, S., Song, C., Zhang, Z., Lu, G., Qiao, C., Hu, Y., Yuen, K. Y., (2020) Structural and functional basis of SARS-CoV-2 entry by using human ACE2. Cell, 181, 894–904.e9.

[12] D. E. Shaw Research, Molecular Dynamics Simulations Related to SARS-CoV-2, 2020. http://www.deshawresearch.com/resources_sarscov2.html.

[13] Li, W., Zhang, C., Sui, J., Kuhn, J. H., Moore, M. J., Luo, S., Wong, S. K., Huang, I. C., Xu, K., Vasilieva, N., (2005) Receptor and viral determinants of SARS-coronavirus adaptation to human ACE2. EMBO J., 24, 1634–1643.

[14] Lavillette, D., Barbouche, R., Yao, Y., Boson, B., Cosset, F. L., Jones, I. M. and Fenouillet, E. (2006) Significant redox insensitivity of the functions of the SARS-CoV spike glycoprotein: comparison with HIV envelope. J. Biol. Chem., 281, 9200–9204.

[15] Li, W., Greenough, T. C., Moore, M. J., Vasilieva, N., Somasundaran, M., Sullivan, J. L., Farzan, M. and Choe, H. (2004) Efficient replication of severe acute respiratory syndrome coronavirus in mouse cells is limited by murine angiotensin-converting enzyme 2. J. Virol., 78, 11429–11433.

[16] Li, F. (2008) Structural analysis of major species barriers between humans and palm civets for severe acute respiratory syndrome coronavirus infections. J. Virol., 82, 6984–6991.

[17] Li, F. (2015) Receptor recognition mechanisms of coronaviruses: a decade of structural studies. J. Virol., 89, 1954–1964.

[18] Lee, H. S., Qi, Y. and Im, W. (2015) Effects of N-glycosylation on protein conformation and dynamics: Protein Data Bank analysis and molecular dynamics simulation study. Sci. Rep., 5, 8926

[19] Wang, Y., Liu, M. and Gao, J. (2020) Enhanced receptor binding of SARS-CoV-2 through networks of hydrogen-bonding and hydrophobic interactions. Proc. Natl. Acad. Sci. USA, 117, 13967–13974.

[20] SCMP (2020) Coronavirus: Hong Kong confirms a second dog is infected. https://www.scmp.com/news/hong-kong/health-environment/article/3075993/coronavirus-hong-kong-confirms-second-dog (Accessed: March 15, 2020).

[21] CASP (2020) CASP and Covid-19. http://predictioncenter.org/caspcommons/index.cgi (Accessed: March 25, 2020).

[22] Institute for Protein Design (2020) Coronavirus (SARS-CoV-2/COVID-19/nCoV-2019). https://www.ipd.uw.edu/coronavirus/ (Accessed on March 20, 2020).

[23] Zhang, C., Zheng, W., Huang, X., Bell, E. W., Zhou, X. and Zhang, Y. (2020) Structure models of all mature peptides in COVID-19 genome by C-I-TASSER, Zhang Lab Univ. Michigan.. https://doi.org/10.1101/2020.02.04.933135V1.

[24] AlphaFold Team. (2020) Computational predictions of protein structures associated with COVID-19. https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19 (Accessed: March 10, 2020).

[25] Zhang, G., ,Pomplun S., Loftis, A.R., Tan, X., Loas, A. and Pentelute, B.L. (2020) Investigation of ACE2 N-terminal fragments binding to SARS-CoV-2 Spike RBD. BioRxiv. 2020.03.19.999318. https://doi.org/10.1101/2020.03.19.999318.

[26] Lupala, C.S., Kumar, V., Li, X., Su, X. and Liu, H. (2020) Computational analysis on the ACE2-derived peptides for neutralizing the ACE2 binding to the spike protein of SARS-CoV-2. BioRxiv. 2020.05.03.075473. https://doi.org/10.1101/2020.05.03.075473.

[27] Han, Y. and Král, P. (2020) Computational design of ACE2-based peptide inhibitors of SARS-CoV-2. ACS Nano., 14, 5143–5147.

[28] Waterhouse, A., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T. A. P., Rempfer, C., Bordoli, L., (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res., 46, W296–W303.

[29] Wu, L., Chen, Q., Liu, K.,  Wang, J., Han, P., Zhang, Y., Hu, Y., Meng, Y., Pan, X., Qiao, C., (2020)  Broad host range of SARS-CoV-2 and the molecular basis for SARS-CoV-2 binding to cat ACE2.  Cell Discov.,  6,  68 https://doi.org/10.1038/s41421-020-00210-9

[30] Anandakrishnan, R., Aguilar, B. and Onufriev, A. V. (2012) H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Res., 40, W537–W541

[31] Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C. and Ferrin, T. E. (2004) UCSF Chimera--a visualization system for exploratory research and analysis. J. Comput. Chem., 25, 1605–1612.

[32] Lee, J., Cheng, X., Swails, J. M., Yeom, M. S., Eastman, P. K., Lemkul, J. A., Wei, S., Buckner, J., Jeong, J. C., Qi, Y., (2016) CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput., 12, 405–413.

[33] Best, R. B., Zhu, X., Shim, J., Lopes, P. E. M., Mittal, J., Feig, M. and Mackerell, A. D. Jr. (2012) Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone j, y and side-chain c₁ and c₂ dihedral angles. J. Chem. Theory Comput., 8, 3257–3273.

[34] Nosé, S. (1984) A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys., 81, 511–519.

[35] Hoover, W. G. (1985) Canonical dynamics: Equilibrium phase-space distributions. Phys. Rev. A Gen. Phys., 31, 1695–1697.

[36] Parrinello, M. and Rahman, A. (1981) Polymorphic transitions in single crystals: A new molecular dynamics method. J. Appl. Phys., 52, 7182–7190.

[37] Abraham, M. J., Murtola, T., Schulz, R., Páll, S., Smith, J. C., Hess, B. and Lindah, E. (2015) Gromacs: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 1–2, 19–25.

[38] Darden, T., York, D. and Pedersen, L. (1993) Particle mesh Ewald: An N·log(N) method for Ewald sums in large systems. J. Chem. Phys., 98, 10089–10092.

[39] Hess, B., Bekker, H., Berendsen, H. J. C. and Fraaije, J. G. E. M. (1997) LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem., 18, 1463–1472

[40] Kumari, R., Kumar, R. and Lynn, A., and the Open Source Drug Discovery Consortium. (2014) g_mmpbsa−a GROMACS tool for high-throughput MM-PBSA calculations. J. Chem. Inf. Model., 54, 1951–1962.

[41] Humphrey, W., Dalke, A. and Schulten, K. (1996) VMD: visual molecular dynamics. J. Mol. Graph., 14, 33–38., 27–28.

ABBREVIATIONS

ACE2 angiotensin converting enzyme 2 protein

RBD receptor binding domain

MD molecular dynamics

SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.15302/J-QB-020-0231.

ACKNOWLEDGEMENTS

The work is supported by Beijing Computational Science Research Center (CSRC) via a director discretionary grant. The authors acknowledge the Beijing Super Cloud Computing Center (BSCC) for providing HPC resources that have contributed to the research. The research is supported by the National Natural Science Foundation (no. U1930402).

COMPLIANCE WITH ETHICS GUIDELINES

The authors Cecylia S. Lupala, Xuanxuan Li, Jian Lei, Hong Chen, Jianxun Qi, Haiguang Liu and Xiao-dong Su declare that they have no competing interests.

All procedures performed in studies were in accordance with the ethical standards of the institution or practice at which the studies were conducted, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

RIGHTS & PERMISSIONS

2021 Higher Education Press